Our group publishes papers presenting new methodologies, describing the results of studies that use our software, and reviewing current topics in the field of Bioinformatics. Scroll down or click here for a complete list of papers produced by our lab. Since 2013, we write blog posts summarizing new research papers and review articles:
GWAS
- Fine Mapping Causal Variants and Allelic Heterogeneity
- Widespread Allelic Heterogeneity in Complex Traits
- Selection in Europeans on Fatty Acid Desaturases Associated with Dietary Changes
- Incorporating prior information into association studies
- Characterization of Expression Quantitative Trait Loci in Pedigrees from Colombia and Costa Rica Ascertained for Bipolar Disorder
- Simultaneous modeling of disease status and clinical phenotypes to increase power in GWAS
- Efficient and accurate multiple-phenotype regression method for high dimensional data considering population structure
- Review Article: Population Structure in Genetic Studies: Confounding Factors and Mixed Models
- Colocalization of GWAS and eQTL Signals Detects Target Genes
- Chromosome conformation elucidates regulatory relationships in developing human brain
Mouse Genetics
- Review Article: The Hybrid Mouse Diversity Panel
- Genes, Environments and Meta-Analysis
- Review Article: Mixed Models and Population Structure
- Identifying Genes Involved in Blood Cell Traits
- Genes, Diet, and Body Weight (in Mice)
- Review Article: Mouse Genetics
Population Structure
- Efficient and accurate multiple-phenotype regression method for high dimensional data considering population structure
- Review Article: Population Structure in Genetic Studies: Confounding Factors and Mixed Models
- Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models
- Multiple testing correction in linear mixed models
- Identification of causal genes for complex traits (CAVIAR-gene)
- Accurate viral population assembly from ultra-deep sequencing data
- GRAT: Speeding up Expression Quantitative Trail Loci (eQTL) Studies
- Correcting Population Structure using Mixed Models Webcast
- Mixed models can correct for population structure for genomic regions under selection
Review Articles
- Review Article: Population Structure in Genetic Studies: Confounding Factors and Mixed Models
- Review Article: The Hybrid Mouse Diversity Panel
- Review Article: GWAS and Missing Heritability
- Review Article: Mixed Models and Population Structure
- Review Article: Mouse Genetics
Publications
Allele Specific Expression Association Priors Association Study Methods Causal Inference Causal Inference Biology Confounding Expression QTLs Fine Mapping Genes By Environment genome-wide association studies Haplotype Phasing Haplotyping from Sequences Heritability HMDP Imputation Meta-Analysis Mixed Models Motif Finding Mouse Genetics Multiple Phenotypes Multiple Testing Pedigree Inference Population Genetics Population Structure Methods Rare Variants RNA sequencing RNAseq Sequence Assembly Sequencing with Pools Structural Variation
2016 |
Lusis, Aldons J; Seldin, Marcus; Allayee, Hooman; Bennett, Brian J; Civelek, Mete; Davis, Richard C; Eskin, Eleazar; Farber, Charles; Hui, Simon T; Mehrabian, Margarete; Norheim, Frode; Pan, Calvin; Parks, Brian; Rau, Christoph; Smith, Desmond J; Vallim, Thomas; Wang, Yibin; Wang, Jessica The Hybrid Mouse Diversity Panel: A Resource for Systems Genetics Analyses of Metabolic and Cardiovascular Traits. Journal Article J Lipid Res, 2016, ISSN: 1539-7262. Abstract | Links | BibTeX | Tags: genome-wide association studies, Hybrid Mouse Diversity Panel, Mouse Genetics @article{Lusis:JLipidRes:2016, title = {The Hybrid Mouse Diversity Panel: A Resource for Systems Genetics Analyses of Metabolic and Cardiovascular Traits.}, author = {Aldons J. Lusis and Marcus Seldin and Hooman Allayee and Brian J. Bennett and Mete Civelek and Richard C. Davis and Eleazar Eskin and Charles Farber and Simon T. Hui and Margarete Mehrabian and Frode Norheim and Calvin Pan and Brian Parks and Christoph Rau and Desmond J. Smith and Thomas Vallim and Yibin Wang and Jessica Wang}, url = {http://dx.doi.org/10.1194/jlr.R066944}, issn = {1539-7262}, year = {2016}, date = {2016-01-01}, journal = {J Lipid Res}, abstract = {The Hybrid Mouse Diversity Panel (HMDP) is a collection of approximately 100 well-characterized inbred strains of mice that can be used to analyze the genetic and environmental factors underlying complex traits. While not nearly as powerful for mapping genetic loci contributing to the traits as human Genome-Wide Association Studies (GWAS), it has some important advantages. First, environmental factors can be controlled. Second, relevant tissues are accessible for global molecular phenotyping. Finally, because inbred strains are renewable, results from separate studies can be integrated. Thus far, the HMDP has been studied for traits relevant to obesity, diabetes, atherosclerosis, osteoporosis, heart failure, immune regulation, fatty liver disease, and host-gut microbiota interactions. High-throughput technologies have been used to examine the genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes of the mice under various environmental conditions. All of the published data are available and can be readily used to formulate hypotheses about genes, pathways and interactions}, keywords = {genome-wide association studies, Hybrid Mouse Diversity Panel, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } The Hybrid Mouse Diversity Panel (HMDP) is a collection of approximately 100 well-characterized inbred strains of mice that can be used to analyze the genetic and environmental factors underlying complex traits. While not nearly as powerful for mapping genetic loci contributing to the traits as human Genome-Wide Association Studies (GWAS), it has some important advantages. First, environmental factors can be controlled. Second, relevant tissues are accessible for global molecular phenotyping. Finally, because inbred strains are renewable, results from separate studies can be integrated. Thus far, the HMDP has been studied for traits relevant to obesity, diabetes, atherosclerosis, osteoporosis, heart failure, immune regulation, fatty liver disease, and host-gut microbiota interactions. High-throughput technologies have been used to examine the genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes of the mice under various environmental conditions. All of the published data are available and can be readily used to formulate hypotheses about genes, pathways and interactions |
Hasin-Brumshtein, Yehudit; Khan, Arshad H; Hormozdiari, Farhad; Pan, Calvin; Parks, Brian W; Petyuk, Vladislav A; Piehowski, Paul D; Brümmer, Anneke; Pellegrini, Matteo; Xiao, Xinshu; Eskin, Eleazar; Smith, Richard D; Lusis, Aldons J; Smith, Desmond J Hypothalamic transcriptomes of 99 mouse strains reveal trans eQTL hotspots, splicing QTLs and novel non-coding genes. Journal Article Elife, 5 , 2016, ISSN: 2050-084X. Abstract | Links | BibTeX | Tags: Expression QTLs, Mouse Genetics @article{HasinBrumshtein:Elife:2016, title = {Hypothalamic transcriptomes of 99 mouse strains reveal trans eQTL hotspots, splicing QTLs and novel non-coding genes.}, author = { Yehudit Hasin-Brumshtein and Arshad H. Khan and Farhad Hormozdiari and Calvin Pan and Brian W. Parks and Vladislav A. Petyuk and Paul D. Piehowski and Anneke Brümmer and Matteo Pellegrini and Xinshu Xiao and Eleazar Eskin and Richard D. Smith and Aldons J. Lusis and Desmond J. Smith}, url = {http://dx.doi.org/10.7554/eLife.15614}, issn = {2050-084X}, year = {2016}, date = {2016-01-01}, journal = {Elife}, volume = {5}, address = {England}, abstract = {Previous studies had shown that the integration of genome wide expression profiles, in metabolic tissues, with genetic and phenotypic variance, provided valuable insight into the underlying molecular mechanisms. We used RNA-Seq to characterize hypothalamic transcriptome in 99 inbred strains of mice from the Hybrid Mouse Diversity Panel (HMDP), a reference resource population for cardiovascular and metabolic traits. We report numerous novel transcripts supported by proteomic analyses, as well as novel non coding RNAs. High resolution genetic mapping of transcript levels in HMDP, reveals both local and trans expression Quantitative Trait Loci (eQTLs) demonstrating 2 trans eQTL 'hotspots' associated with expression of hundreds of genes. We also report thousands of alternative splicing events regulated by genetic variants. Finally, comparison with about 150 metabolic and cardiovascular traits revealed many highly significant associations. Our data provide a rich resource for understanding the many physiologic functions mediated by the hypothalamus and their genetic regulation}, keywords = {Expression QTLs, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Previous studies had shown that the integration of genome wide expression profiles, in metabolic tissues, with genetic and phenotypic variance, provided valuable insight into the underlying molecular mechanisms. We used RNA-Seq to characterize hypothalamic transcriptome in 99 inbred strains of mice from the Hybrid Mouse Diversity Panel (HMDP), a reference resource population for cardiovascular and metabolic traits. We report numerous novel transcripts supported by proteomic analyses, as well as novel non coding RNAs. High resolution genetic mapping of transcript levels in HMDP, reveals both local and trans expression Quantitative Trait Loci (eQTLs) demonstrating 2 trans eQTL 'hotspots' associated with expression of hundreds of genes. We also report thousands of alternative splicing events regulated by genetic variants. Finally, comparison with about 150 metabolic and cardiovascular traits revealed many highly significant associations. Our data provide a rich resource for understanding the many physiologic functions mediated by the hypothalamus and their genetic regulation |
Lavinsky, Joel; Ge, Marshall; Crow, Amanda L; Pan, Calvin; Wang, Juemei; Dermanaki, Pehzman Salehi; Myint, Anthony; Eskin, Eleazar; Allayee, Hooman; Lusis, Aldons J; Friedman, Rick A The Genetic Architecture of Noise-induced Hearing Loss: Evidence for a Gene-by-Environment Interaction. Journal Article G3 (Bethesda), 2016, ISSN: 2160-1836. Abstract | Links | BibTeX | Tags: Mouse Genetics @article{Lavinsky:G3:2016, title = {The Genetic Architecture of Noise-induced Hearing Loss: Evidence for a Gene-by-Environment Interaction.}, author = { Joel Lavinsky and Marshall Ge and Amanda L. Crow and Calvin Pan and Juemei Wang and Pehzman Salehi Dermanaki and Anthony Myint and Eleazar Eskin and Hooman Allayee and Aldons J. Lusis and Rick A. Friedman}, url = {http://dx.doi.org/10.1534/g3.116.032516}, issn = {2160-1836}, year = {2016}, date = {2016-01-01}, journal = {G3 (Bethesda)}, abstract = {The discovery of environmentally specific genetic effects is crucial to the understanding of complex traits, such as susceptibility to noise-induced hearing loss (NIHL). In this manuscript we describe the first genome-wide association study (GWAS) for NIHL in a large and well-characterized population of inbred mouse strains known as the Hybrid Mouse Diversity Panel (HMDP). We recorded auditory brainstem response (ABR) thresholds both pre and post 2-hour exposure to 10 kHz octave band noise at 108 dB SPL (sound pressure level) in 5-6 week-old female mice from the HMDP (4-5 mice/strain). From the observation that NIHL susceptibility varied among the strains, we performed a GWAS with correction for population structure and mapped a locus on chromosome 6 that was statistically significantly associated with two adjacent frequencies. We then used a 'genetical genomics' approach that included the analysis of cochlear eQTLs to identify candidate genes within the GWAS QTL. In order to validate the gene-by-environment interaction, we compared the effects of the post noise exposure locus with that from the same unexposed strains. The most significant SNP at chromosome 6 (rs37517079) was associated with noise susceptibility, but was not significant at the same frequencies in our unexposed study. These findings demonstrate that the genetic architecture of NIHL is distinct from that of unexposed hearing levels and provide strong evidence for gene-by-environment interactions in NIHL}, keywords = {Mouse Genetics}, pubstate = {published}, tppubtype = {article} } The discovery of environmentally specific genetic effects is crucial to the understanding of complex traits, such as susceptibility to noise-induced hearing loss (NIHL). In this manuscript we describe the first genome-wide association study (GWAS) for NIHL in a large and well-characterized population of inbred mouse strains known as the Hybrid Mouse Diversity Panel (HMDP). We recorded auditory brainstem response (ABR) thresholds both pre and post 2-hour exposure to 10 kHz octave band noise at 108 dB SPL (sound pressure level) in 5-6 week-old female mice from the HMDP (4-5 mice/strain). From the observation that NIHL susceptibility varied among the strains, we performed a GWAS with correction for population structure and mapped a locus on chromosome 6 that was statistically significantly associated with two adjacent frequencies. We then used a 'genetical genomics' approach that included the analysis of cochlear eQTLs to identify candidate genes within the GWAS QTL. In order to validate the gene-by-environment interaction, we compared the effects of the post noise exposure locus with that from the same unexposed strains. The most significant SNP at chromosome 6 (rs37517079) was associated with noise susceptibility, but was not significant at the same frequencies in our unexposed study. These findings demonstrate that the genetic architecture of NIHL is distinct from that of unexposed hearing levels and provide strong evidence for gene-by-environment interactions in NIHL |
2015 |
Rau, Christoph D; Parks, Brian; Wang, Yibin; Eskin, Eleazar; Simecek, Petr; Churchill, Gary A; Lusis, Aldons J High Density Genotypes of Inbred Mouse Strains: Improved Power and Precision of Association Mapping. Journal Article G3 (Bethesda), 5 (10), pp. 2021-6, 2015, ISSN: 2160-1836. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Rau:G3:2015b, title = {High Density Genotypes of Inbred Mouse Strains: Improved Power and Precision of Association Mapping.}, author = { Christoph D. Rau and Brian Parks and Yibin Wang and Eleazar Eskin and Petr Simecek and Gary A. Churchill and Aldons J. Lusis}, url = {http://dx.doi.org/10.1534/g3.115.020784}, issn = {2160-1836}, year = {2015}, date = {2015-01-01}, journal = {G3 (Bethesda)}, volume = {5}, number = {10}, pages = {2021-6}, address = {United States}, abstract = {Human genome-wide association studies (GWAS) have identified thousands of loci associated with disease phenotypes. GWAS studies have also become feasible using rodent models and these have some important advantages over human studies including controlled environment, access to tissues for molecular profiling, reproducible genotypes and a wide array of techniques for experimental validation. Association mapping with common mouse inbred strains generally requires one hundred or more strains to achieve sufficient power and mapping resolution; in contrast, sample sizes for human studies are typically one or more orders of magnitude greater than this. To enable well-powered studies in mice, we have generated high-density genotypes for ~175 inbred strains of mice using the Mouse Diversity Array. These new data increase marker density by 1.9-fold, have reduced missing data rates, and provide more accurate identification of heterozygous regions compared to previous genotype data. We report the discovery of new loci from previously reported association mapping studies using the new genotype data. The data are freely available for download and web-based tools provide easy access for association mapping and viewing of the underlying intensity data for individual loci}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Human genome-wide association studies (GWAS) have identified thousands of loci associated with disease phenotypes. GWAS studies have also become feasible using rodent models and these have some important advantages over human studies including controlled environment, access to tissues for molecular profiling, reproducible genotypes and a wide array of techniques for experimental validation. Association mapping with common mouse inbred strains generally requires one hundred or more strains to achieve sufficient power and mapping resolution; in contrast, sample sizes for human studies are typically one or more orders of magnitude greater than this. To enable well-powered studies in mice, we have generated high-density genotypes for ~175 inbred strains of mice using the Mouse Diversity Array. These new data increase marker density by 1.9-fold, have reduced missing data rates, and provide more accurate identification of heterozygous regions compared to previous genotype data. We report the discovery of new loci from previously reported association mapping studies using the new genotype data. The data are freely available for download and web-based tools provide easy access for association mapping and viewing of the underlying intensity data for individual loci |
2014 |
Kang, Eun Yong; Han, Buhm; Furlotte, Nicholas; Joo, Jong Wha J; Shih, Diana; Davis, Richard C; Lusis, Aldons J; Eskin, Eleazar Meta-Analysis Identifies Gene-by-Environment Interactions as Demonstrated in a Study of 4,965 Mice Journal Article PLoS Genet, 10 (1), pp. e1004022, 2014, ISSN: 1553-7404. Abstract | Links | BibTeX | Tags: Genes By Environment, Meta-Analysis, Mouse Genetics @article{10.1371/journal.pgen.1004022, title = {Meta-Analysis Identifies Gene-by-Environment Interactions as Demonstrated in a Study of 4,965 Mice}, author = { Eun Yong Kang and Buhm Han and Nicholas Furlotte and Jong Wha J. Joo and Diana Shih and Richard C. Davis and Aldons J. Lusis and Eleazar Eskin}, url = {http://dx.doi.org/10.1371%2Fjournal.pgen.1004022}, issn = {1553-7404}, year = {2014}, date = {2014-01-01}, journal = {PLoS Genet}, volume = {10}, number = {1}, pages = {e1004022}, publisher = {Public Library of Science}, abstract = {Author Summary Identifying gene-by-environment interactions is important for understand the architecture of a complex trait. Discovering gene-by-environment interaction requires the observation of the same phenotype in individuals under different environments. Model organism studies are often conducted under different environments. These studies provide an unprecedented opportunity for researchers to identify the gene-by-environment interactions. A difference in the effect size of a genetic variant between two studies conducted in different environments may suggest the presence of a gene-by-environment interaction. In this paper, we propose to employ a random-effect-based meta-analysis approach to identify gene-by-environment interaction, which assumes different or heterogeneous effect sizes between studies. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional approaches for discovery of gene-by-environment interactions, which treats the gene-by-environment interactions as covariates in the analysis. We provide a intuitive way to visualize the results of the meta-analysis at a locus which allows us to obtain the biological insights of gene-by-environment interactions. We demonstrate our method by searching for gene-by-environment interactions by combining 17 mouse genetic studies totaling 4,965 distinct animals.}, keywords = {Genes By Environment, Meta-Analysis, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Author Summary Identifying gene-by-environment interactions is important for understand the architecture of a complex trait. Discovering gene-by-environment interaction requires the observation of the same phenotype in individuals under different environments. Model organism studies are often conducted under different environments. These studies provide an unprecedented opportunity for researchers to identify the gene-by-environment interactions. A difference in the effect size of a genetic variant between two studies conducted in different environments may suggest the presence of a gene-by-environment interaction. In this paper, we propose to employ a random-effect-based meta-analysis approach to identify gene-by-environment interaction, which assumes different or heterogeneous effect sizes between studies. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional approaches for discovery of gene-by-environment interactions, which treats the gene-by-environment interactions as covariates in the analysis. We provide a intuitive way to visualize the results of the meta-analysis at a locus which allows us to obtain the biological insights of gene-by-environment interactions. We demonstrate our method by searching for gene-by-environment interactions by combining 17 mouse genetic studies totaling 4,965 distinct animals. |
Ohmen, Jeffrey; Kang, Eun Yong ; Li, Xin ; Joo, Jong Wha ; Hormozdiari, Farhad ; Zheng, Qing Yin ; Davis, Richard C; Lusis, Aldons J; Eskin, Eleazar ; Friedman, Rick A Genome-Wide Association Study for Age-Related Hearing Loss (AHL) in the Mouse: A Meta-Analysis. Journal Article J Assoc Res Otolaryngol, 15 (3), pp. 335-52, 2014, ISSN: 1438-7573. Abstract | Links | BibTeX | Tags: HMDP, Meta-Analysis, Mouse Genetics @article{Ohmen:JAssocResOtolaryngol:2014, title = {Genome-Wide Association Study for Age-Related Hearing Loss (AHL) in the Mouse: A Meta-Analysis.}, author = { Jeffrey Ohmen and Eun Yong Kang and Xin Li and Jong Wha Joo and Farhad Hormozdiari and Qing Yin Zheng and Richard C. Davis and Aldons J. Lusis and Eleazar Eskin and Rick A. Friedman}, url = {http://dx.doi.org/10.1007/s10162-014-0443-2}, issn = {1438-7573}, year = {2014}, date = {2014-01-01}, journal = {J Assoc Res Otolaryngol}, volume = {15}, number = {3}, pages = {335-52}, address = {United States}, abstract = {Age-related hearing loss (AHL) is characterized by a symmetric sensorineural hearing loss primarily in high frequencies and individuals have different levels of susceptibility to AHL. Heritability studies have shown that the sources of this variance are both genetic and environmental, with approximately half of the variance attributable to hereditary factors as reported by Huag and Tang (Eur Arch Otorhinolaryngol 267(8):1179-1191, 2010). Only a limited number of large-scale association studies for AHL have been undertaken in humans, to date. An alternate and complementary approach to these human studies is through the use of mouse models. Advantages of mouse models include that the environment can be more carefully controlled, measurements can be replicated in genetically identical animals, and the proportion of the variability explained by genetic variation is increased. Complex traits in mouse strains have been shown to have higher heritability and genetic loci often have stronger effects on the trait compared to humans. Motivated by these advantages, we have performed the first genome-wide association study of its kind in the mouse by combining several data sets in a meta-analysis to identify loci associated with age-related hearing loss. We identified five genome-wide significant loci (<10(-6)). One of these loci confirmed a previously identified locus (ahl8) on distal chromosome 11 and greatly narrowed the candidate region. Specifically, the most significant associated SNP is located 450udotkb upstream of Fscn2. These data confirm the utility of this approach and provide new high-resolution mapping information about variation within the mouse genome associated with hearing loss}, keywords = {HMDP, Meta-Analysis, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Age-related hearing loss (AHL) is characterized by a symmetric sensorineural hearing loss primarily in high frequencies and individuals have different levels of susceptibility to AHL. Heritability studies have shown that the sources of this variance are both genetic and environmental, with approximately half of the variance attributable to hereditary factors as reported by Huag and Tang (Eur Arch Otorhinolaryngol 267(8):1179-1191, 2010). Only a limited number of large-scale association studies for AHL have been undertaken in humans, to date. An alternate and complementary approach to these human studies is through the use of mouse models. Advantages of mouse models include that the environment can be more carefully controlled, measurements can be replicated in genetically identical animals, and the proportion of the variability explained by genetic variation is increased. Complex traits in mouse strains have been shown to have higher heritability and genetic loci often have stronger effects on the trait compared to humans. Motivated by these advantages, we have performed the first genome-wide association study of its kind in the mouse by combining several data sets in a meta-analysis to identify loci associated with age-related hearing loss. We identified five genome-wide significant loci (<10(-6)). One of these loci confirmed a previously identified locus (ahl8) on distal chromosome 11 and greatly narrowed the candidate region. Specifically, the most significant associated SNP is located 450udotkb upstream of Fscn2. These data confirm the utility of this approach and provide new high-resolution mapping information about variation within the mouse genome associated with hearing loss |
2013 |
Leikauf, George D; Concel, Vincent J; Bein, Kiflai ; Liu, Pengyuan ; Berndt, Annerose ; Martin, Timothy M; Ganguly, Koustav ; Jang, An-Soo S; Brant, Kelly A; Jr, Richard Dopico A; Upadhyay, Swapna ; Cario, Clinton L; Di, Yp Peter ; Vuga, Louis J; Kostem, Emrah ; Eskin, Eleazar ; You, Ming ; Kaminski, Naftali ; Prows, Daniel R; Knoell, Daren L; Fabisiak, James P Functional Genomic Assessment of Phosgene-induced Acute Lung Injury in Mice. Journal Article Am J Respir Cell Mol Biol, 2013, ISSN: 1535-4989. Abstract | Links | BibTeX | Tags: Mouse Genetics @article{Leikauf:AmJRespirCellMolBiol:2013, title = {Functional Genomic Assessment of Phosgene-induced Acute Lung Injury in Mice.}, author = { George D. Leikauf and Vincent J. Concel and Kiflai Bein and Pengyuan Liu and Annerose Berndt and Timothy M. Martin and Koustav Ganguly and An-Soo S. Jang and Kelly A. Brant and Richard A. Dopico Jr and Swapna Upadhyay and Clinton L. Cario and Yp Peter Di and Louis J. Vuga and Emrah Kostem and Eleazar Eskin and Ming You and Naftali Kaminski and Daniel R. Prows and Daren L. Knoell and James P. Fabisiak}, url = {http://dx.doi.org/10.1165/rcmb.2012-0337OC}, issn = {1535-4989}, year = {2013}, date = {2013-01-01}, journal = {Am J Respir Cell Mol Biol}, organization = {University of Pittsburgh, Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, Pennsylvania, United States ; gleikauf@pitt.edu.}, abstract = {In this study, a genetically-diverse panel of 43 mouse strains was exposed to phosgene and genome-wide association mapping performed employing a high density SNP assembly. Transcriptomic analysis was also used to improve the genetic resolution in the identification of genetic determinants of phosgene-induced acute lung injury. We prioritized the identified genes based on whether the encoded protein was previously associated with lung injury or contained a nonsynonymous SNP within a functional domain. In addition, candidates were selected that contained a promoter SNP that could alter a putative transcription factor binding site and had variable expression by transcriptomic analyses. The latter 2 criteria also required that 10% of mice carried the minor allele and that this allele could account for 10% of the phenotypic difference noted between the strains at the phenotypic extremes. This integrative functional approach revealed 14 candidate genes that included Atp1a1, Alox5, Galnt11, Hrh1, Mbd4, Phactr2, Plxnd1, Ptprt, Reln, and Zfand4, which had significant SNP associations, and Itga9, Man1a2, Mapk14, and Vwf, which had suggestive SNP associations. Of the genes with significant SNP associations, Atp1a1, Alox5, Plxnd1, Ptprt, and Zfand4 are particularly noteworthy and could be associated with acute lung injury in several ways. Using a competitive electrophoretic mobility shift analysis, Atp1a1 promoter (rs215053185) oligonucleotide containing the minor G-allele formed a major distinct faster-migrating complex. In addition, a gene with a suggestive SNP association, Itga9, is linked to TGFB1-signaling, which previously has been associated with the susceptibility to acute lung injury in mice}, keywords = {Mouse Genetics}, pubstate = {published}, tppubtype = {article} } In this study, a genetically-diverse panel of 43 mouse strains was exposed to phosgene and genome-wide association mapping performed employing a high density SNP assembly. Transcriptomic analysis was also used to improve the genetic resolution in the identification of genetic determinants of phosgene-induced acute lung injury. We prioritized the identified genes based on whether the encoded protein was previously associated with lung injury or contained a nonsynonymous SNP within a functional domain. In addition, candidates were selected that contained a promoter SNP that could alter a putative transcription factor binding site and had variable expression by transcriptomic analyses. The latter 2 criteria also required that 10% of mice carried the minor allele and that this allele could account for 10% of the phenotypic difference noted between the strains at the phenotypic extremes. This integrative functional approach revealed 14 candidate genes that included Atp1a1, Alox5, Galnt11, Hrh1, Mbd4, Phactr2, Plxnd1, Ptprt, Reln, and Zfand4, which had significant SNP associations, and Itga9, Man1a2, Mapk14, and Vwf, which had suggestive SNP associations. Of the genes with significant SNP associations, Atp1a1, Alox5, Plxnd1, Ptprt, and Zfand4 are particularly noteworthy and could be associated with acute lung injury in several ways. Using a competitive electrophoretic mobility shift analysis, Atp1a1 promoter (rs215053185) oligonucleotide containing the minor G-allele formed a major distinct faster-migrating complex. In addition, a gene with a suggestive SNP association, Itga9, is linked to TGFB1-signaling, which previously has been associated with the susceptibility to acute lung injury in mice |
Parks, Brian W; Nam, Elizabeth ; Org, Elin ; Kostem, Emrah ; Norheim, Frode ; Hui, Simon T; Pan, Calvin ; Civelek, Mete ; Rau, Christoph D; Bennett, Brian J; Mehrabian, Margarete ; Ursell, Luke K; He, Aiqing ; Castellani, Lawrence W; Zinker, Bradley ; Kirby, Mark ; Drake, Thomas A; Drevon, Christian A; Knight, Rob ; Gargalovic, Peter ; Kirchgessner, Todd ; Eskin, Eleazar ; Lusis, Aldons J Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice. Journal Article Cell Metab, 17 (1), pp. 141-52, 2013, ISSN: 1932-7420. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Parks:CellMetab:2013, title = {Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice.}, author = { Brian W. Parks and Elizabeth Nam and Elin Org and Emrah Kostem and Frode Norheim and Simon T. Hui and Calvin Pan and Mete Civelek and Christoph D. Rau and Brian J. Bennett and Margarete Mehrabian and Luke K. Ursell and Aiqing He and Lawrence W. Castellani and Bradley Zinker and Mark Kirby and Thomas A. Drake and Christian A. Drevon and Rob Knight and Peter Gargalovic and Todd Kirchgessner and Eleazar Eskin and Aldons J. Lusis}, url = {http://dx.doi.org/10.1016/j.cmet.2012.12.007}, issn = {1932-7420}, year = {2013}, date = {2013-01-01}, journal = {Cell Metab}, volume = {17}, number = {1}, pages = {141-52}, address = {United States}, organization = {Department of Medicine/Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: bparks@mednet.ucla.edu.}, abstract = {Obesity is a highly heritable disease driven by complex interactions between genetic and environmental factors. Human genome-wide association studies (GWAS) have identified a number of loci contributing to obesity; however, a major limitation of these studies is the inability to assess environmental interactions common to obesity. Using a systems genetics approach, we measured obesity traits, global gene expression, and gut microbiota composition in response to a high-fat/high-sucrose (HF/HS) diet of more than 100 inbred strains of mice. Here we show that HF/HS feeding promotes robust, strain-specific changes in obesity that are not accounted for by food intake and provide evidence for a genetically determined set point for obesity. GWAS analysis identified 11 genome-wide significant loci associated with obesity traits, several of which overlap with loci identified in human studies. We also show strong relationships between genotype and gut microbiota plasticity during HF/HS feeding and identify gut microbial phylotypes associated with obesity}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Obesity is a highly heritable disease driven by complex interactions between genetic and environmental factors. Human genome-wide association studies (GWAS) have identified a number of loci contributing to obesity; however, a major limitation of these studies is the inability to assess environmental interactions common to obesity. Using a systems genetics approach, we measured obesity traits, global gene expression, and gut microbiota composition in response to a high-fat/high-sucrose (HF/HS) diet of more than 100 inbred strains of mice. Here we show that HF/HS feeding promotes robust, strain-specific changes in obesity that are not accounted for by food intake and provide evidence for a genetically determined set point for obesity. GWAS analysis identified 11 genome-wide significant loci associated with obesity traits, several of which overlap with loci identified in human studies. We also show strong relationships between genotype and gut microbiota plasticity during HF/HS feeding and identify gut microbial phylotypes associated with obesity |
Davis, Richard C; van Nas, Atila ; Bennett, Brian ; Orozco, Luz ; Pan, Calvin ; Rau, Christoph D; Eskin, Eleazar ; Lusis, Aldons J Genome-wide association mapping of blood cell traits in mice. Journal Article Mamm Genome, 2013, ISSN: 1432-1777. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Davis:MammGenome:2013, title = {Genome-wide association mapping of blood cell traits in mice.}, author = { Richard C. Davis and Atila van Nas and Brian Bennett and Luz Orozco and Calvin Pan and Christoph D. Rau and Eleazar Eskin and Aldons J. Lusis}, url = {http://dx.doi.org/10.1007/s00335-013-9448-0}, issn = {1432-1777}, year = {2013}, date = {2013-01-01}, journal = {Mamm Genome}, organization = {Department of Medicine/Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.}, abstract = {Genetic variations in blood cell parameters can impact clinical traits. We report here the mapping of blood cell traits in a panel of 100 inbred strains of mice of the Hybrid Mouse Diversity Panel (HMDP) using genome-wide association (GWA). We replicated a locus previously identified in using linkage analysis in several genetic crosses for mean corpuscular volume (MCV) and a number of other red blood cell traits on distal chromosome 7. Our peak for SNP association to MCV occurred in a linkage disequilibrium (LD) block spanning from 109.38 to 111.75Mb that includes Hbb-b1, the likely causal gene. Altogether, we identified five loci controlling red blood cell traits (on chromosomes 1, 7, 11, 12, and 16), and four of these correspond to loci for red blood cell traits reported in a recent human GWA study. For white blood cells, including granulocytes, monocytes, and lymphocytes, a total of six significant loci were identified on chromosomes 1, 6, 8, 11, 12, and 15. An average of ten candidate genes were found at each locus and those were prioritized by examining functional variants in the HMDP such as missense and expression variants. These results provide intermediate phenotypes and candidate loci for genetic studies of atherosclerosis and cancer as well as inflammatory and immune disorders in mice}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Genetic variations in blood cell parameters can impact clinical traits. We report here the mapping of blood cell traits in a panel of 100 inbred strains of mice of the Hybrid Mouse Diversity Panel (HMDP) using genome-wide association (GWA). We replicated a locus previously identified in using linkage analysis in several genetic crosses for mean corpuscular volume (MCV) and a number of other red blood cell traits on distal chromosome 7. Our peak for SNP association to MCV occurred in a linkage disequilibrium (LD) block spanning from 109.38 to 111.75Mb that includes Hbb-b1, the likely causal gene. Altogether, we identified five loci controlling red blood cell traits (on chromosomes 1, 7, 11, 12, and 16), and four of these correspond to loci for red blood cell traits reported in a recent human GWA study. For white blood cells, including granulocytes, monocytes, and lymphocytes, a total of six significant loci were identified on chromosomes 1, 6, 8, 11, 12, and 15. An average of ten candidate genes were found at each locus and those were prioritized by examining functional variants in the HMDP such as missense and expression variants. These results provide intermediate phenotypes and candidate loci for genetic studies of atherosclerosis and cancer as well as inflammatory and immune disorders in mice |
2012 |
Flint, Jonathan; Eskin, Eleazar Genome-wide association studies in mice Journal Article Nature Reviews Genetics, 13 (11), pp. 807-17, 2012, ISSN: 1471-0064. Abstract | Links | BibTeX | Tags: Mouse Genetics @article{Flint:NatureReviewsGenetics:2012, title = {Genome-wide association studies in mice}, author = { Jonathan Flint and Eleazar Eskin}, url = {http://dx.doi.org/10.1038/nrg3335}, issn = {1471-0064}, year = {2012}, date = {2012-01-01}, journal = {Nature Reviews Genetics}, volume = {13}, number = {11}, pages = {807-17}, publisher = {Nature Publishing Group}, address = {England}, abstract = {Genome-wide association studies (GWASs) have transformed the field of human genetics and have led to the discovery of hundreds of genes that are implicated in human disease. The technological advances that drove this revolution are now poised to transform genetic studies in model organisms, including mice. However, the design of GWASs in mouse strains is fundamentally different from the design of human GWASs, creating new challenges and opportunities. This Review gives an overview of the novel study designs for mouse GWASs, which dramatically improve both the statistical power and resolution compared to classical gene-mapping approaches.}, keywords = {Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Genome-wide association studies (GWASs) have transformed the field of human genetics and have led to the discovery of hundreds of genes that are implicated in human disease. The technological advances that drove this revolution are now poised to transform genetic studies in model organisms, including mice. However, the design of GWASs in mouse strains is fundamentally different from the design of human GWASs, creating new challenges and opportunities. This Review gives an overview of the novel study designs for mouse GWASs, which dramatically improve both the statistical power and resolution compared to classical gene-mapping approaches. |
Bennett, Brian J; Orozco, Luz ; Kostem, Emrah ; Erbilgin, Ayca ; Dallinga, Marchien ; Neuhaus, Isaac ; Guan, Bo ; Wang, Xuping ; Eskin, Eleazar ; Lusis, Aldons J High-Resolution Association Mapping of Atherosclerosis Loci in Mice. Journal Article Arterioscler Thromb Vasc Biol, 2012, ISSN: 1524-4636. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Bennett:ArteriosclerThrombVascBiol:2012, title = {High-Resolution Association Mapping of Atherosclerosis Loci in Mice.}, author = { Brian J. Bennett and Luz Orozco and Emrah Kostem and Ayca Erbilgin and Marchien Dallinga and Isaac Neuhaus and Bo Guan and Xuping Wang and Eleazar Eskin and Aldons J. Lusis}, url = {http://dx.doi.org/10.1161/ATVBAHA.112.253864}, issn = {1524-4636}, year = {2012}, date = {2012-01-01}, journal = {Arterioscler Thromb Vasc Biol}, organization = {Department of Genetics, University of North Carolina, Chapel Hill, NC.}, abstract = {OBJECTIVE: The purpose of this study was to fine map previously identified quantitative trait loci affecting atherosclerosis in mice using association analysis. METHODS AND RESULTS: We recently showed that high-resolution association analysis using common inbred strains of mice is feasible if corrected for population structure. To use this approach for atherosclerosis, which requires a sensitizing mutation, we bred human apolipoprotein B-100 transgenic mice with 22 different inbred strains to produce F1 heterozygotes. Mice carrying the dominant transgene were tested for association with high-density single nucleotide polymorphism maps. Here, we focus on high-resolution mapping of the previously described atherosclerosis 30 locus on chromosome 1. Compared with the previous linkage analysis, association improved the resolution of the atherosclerosis 30 locus by more than an order of magnitude. Using expression quantitative trait locus analysis, we identified one of the genes in the region, desmin, as a strong candidate. CONCLUSIONS: Our high-resolution mapping approach accurately identifies and fine maps known atherosclerosis quantitative trait loci. These results suggest that high-resolution genome-wide association analysis for atherosclerosis is feasible in mice.}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } OBJECTIVE: The purpose of this study was to fine map previously identified quantitative trait loci affecting atherosclerosis in mice using association analysis. METHODS AND RESULTS: We recently showed that high-resolution association analysis using common inbred strains of mice is feasible if corrected for population structure. To use this approach for atherosclerosis, which requires a sensitizing mutation, we bred human apolipoprotein B-100 transgenic mice with 22 different inbred strains to produce F1 heterozygotes. Mice carrying the dominant transgene were tested for association with high-density single nucleotide polymorphism maps. Here, we focus on high-resolution mapping of the previously described atherosclerosis 30 locus on chromosome 1. Compared with the previous linkage analysis, association improved the resolution of the atherosclerosis 30 locus by more than an order of magnitude. Using expression quantitative trait locus analysis, we identified one of the genes in the region, desmin, as a strong candidate. CONCLUSIONS: Our high-resolution mapping approach accurately identifies and fine maps known atherosclerosis quantitative trait loci. These results suggest that high-resolution genome-wide association analysis for atherosclerosis is feasible in mice. |
Ghazalpour, Anatole; Rau, Christoph D; Farber, Charles R; Bennett, Brian J; Orozco, Luz D; van Nas, Atila; Pan, Calvin; Allayee, Hooman; Beaven, Simon W; Civelek, Mete; Davis, Richard C; Drake, Thomas A; Friedman, Rick A; Furlotte, Nick; Hui, Simon T; Jentsch, David J; Kostem, Emrah; Kang, Hyun Min; Kang, Eun Yong; Joo, Jong Wha; Korshunov, Vyacheslav A; Laughlin, Rick E; Martin, Lisa J; Ohmen, Jeffrey D; Parks, Brian W; Pellegrini, Matteo; Reue, Karen; Smith, Desmond J; Tetradis, Sotirios; Wang, Jessica; Wang, Yibin; N, James Hybrid mouse diversity panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits. Journal Article Mamm Genome, 2012, ISSN: 1432-1777. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Ghazalpour:MammGenome:2012, title = {Hybrid mouse diversity panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits.}, author = {Anatole Ghazalpour and Christoph D Rau and Charles R Farber and Brian J Bennett and Luz D Orozco and Atila van Nas and Calvin Pan and Hooman Allayee and Simon W Beaven and Mete Civelek and Richard C Davis and Thomas A Drake and Rick A Friedman and Nick Furlotte and Simon T Hui and J David Jentsch and Emrah Kostem and Hyun Min Kang and Eun Yong Kang and Jong Wha Joo and Vyacheslav A Korshunov and Rick E Laughlin and Lisa J Martin and Jeffrey D Ohmen and Brian W Parks and Matteo Pellegrini and Karen Reue and Desmond J Smith and Sotirios Tetradis and Jessica Wang and Yibin Wang and James N}, url = {http://dx.doi.org/10.1007/s00335-012-9411-5}, issn = {1432-1777}, year = {2012}, date = {2012-01-01}, journal = {Mamm Genome}, organization = {Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.}, abstract = {We have developed an association-based approach using classical inbred strains of mice in which we correct for population structure, which is very extensive in mice, using an efficient mixed-model algorithm. Our approach includes inbred parental strains as well as recombinant inbred strains in order to capture loci with effect sizes typical of complex traits in mice (in the range of 5% of total trait variance). Over the last few years, we have typed the hybrid mouse diversity panel (HMDP) strains for a variety of clinical traits as well as intermediate phenotypes and have shown that the HMDP has sufficient power to map genes for highly complex traits with resolution that is in most cases less than a megabase. In this essay, we review our experience with the HMDP, describe various ongoing projects, and discuss how the HMDP may fit into the larger picture of common diseases and different approaches.}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } We have developed an association-based approach using classical inbred strains of mice in which we correct for population structure, which is very extensive in mice, using an efficient mixed-model algorithm. Our approach includes inbred parental strains as well as recombinant inbred strains in order to capture loci with effect sizes typical of complex traits in mice (in the range of 5% of total trait variance). Over the last few years, we have typed the hybrid mouse diversity panel (HMDP) strains for a variety of clinical traits as well as intermediate phenotypes and have shown that the HMDP has sufficient power to map genes for highly complex traits with resolution that is in most cases less than a megabase. In this essay, we review our experience with the HMDP, describe various ongoing projects, and discuss how the HMDP may fit into the larger picture of common diseases and different approaches. |
Hersch, Micha; Peter, Bastian ; Kang, Hyun Min ; Schüpfer, Fanny ; Abriel, Hugues ; Pedrazzini, Thierry ; Eskin, Eleazar ; Beckmann, Jacques S; Bergmann, Sven ; Maurer, Fabienne Mapping genetic variants associated with Beta-adrenergic responses in inbred mice. Journal Article PLoS One, 7 (7), pp. e41032, 2012, ISSN: 1932-6203. Abstract | Links | BibTeX | Tags: Mouse Genetics @article{Hersch:PlosOne:2012, title = {Mapping genetic variants associated with Beta-adrenergic responses in inbred mice.}, author = { Micha Hersch and Bastian Peter and Hyun Min Kang and Fanny Schüpfer and Hugues Abriel and Thierry Pedrazzini and Eleazar Eskin and Jacques S. Beckmann and Sven Bergmann and Fabienne Maurer}, url = {http://dx.doi.org/10.1371/journal.pone.0041032}, issn = {1932-6203}, year = {2012}, date = {2012-01-01}, journal = {PLoS One}, volume = {7}, number = {7}, pages = {e41032}, address = {United States}, organization = {Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland.}, abstract = {$beta$-blockers and $beta$-agonists are primarily used to treat cardiovascular diseases. Inter-individual variability in response to both drug classes is well recognized, yet the identity and relative contribution of the genetic players involved are poorly understood. This work is the first genome-wide association study (GWAS) addressing the values and susceptibility of cardiovascular-related traits to a selective $beta$(1)-blocker, Atenolol (ate), and a $beta$-agonist, Isoproterenol (iso). The phenotypic dataset consisted of 27 highly heritable traits, each measured across 22 inbred mouse strains and four pharmacological conditions. The genotypic panel comprised 79922 informative SNPs of the mouse HapMap resource. Associations were mapped by Efficient Mixed Model Association (EMMA), a method that corrects for the population structure and genetic relatedness of the various strains. A total of 205 separate genome-wide scans were analyzed. The most significant hits include three candidate loci related to cardiac and body weight, three loci for electrocardiographic (ECG) values, two loci for the susceptibility of atrial weight index to iso, four loci for the susceptibility of systolic blood pressure (SBP) to perturbations of the $beta$-adrenergic system, and one locus for the responsiveness of QTc (p<10(-8)). An additional 60 loci were suggestive for one or the other of the 27 traits, while 46 others were suggestive for one or the other drug effects (p<10(-6)). Most hits tagged unexpected regions, yet at least two loci for the susceptibility of SBP to $beta$-adrenergic drugs pointed at members of the hypothalamic-pituitary-thyroid axis. Loci for cardiac-related traits were preferentially enriched in genes expressed in the heart, while 23% of the testable loci were replicated with datasets of the Mouse Phenome Database (MPD). Altogether these data and validation tests indicate that the mapped loci are relevant to the traits and responses studied.}, keywords = {Mouse Genetics}, pubstate = {published}, tppubtype = {article} } $beta$-blockers and $beta$-agonists are primarily used to treat cardiovascular diseases. Inter-individual variability in response to both drug classes is well recognized, yet the identity and relative contribution of the genetic players involved are poorly understood. This work is the first genome-wide association study (GWAS) addressing the values and susceptibility of cardiovascular-related traits to a selective $beta$(1)-blocker, Atenolol (ate), and a $beta$-agonist, Isoproterenol (iso). The phenotypic dataset consisted of 27 highly heritable traits, each measured across 22 inbred mouse strains and four pharmacological conditions. The genotypic panel comprised 79922 informative SNPs of the mouse HapMap resource. Associations were mapped by Efficient Mixed Model Association (EMMA), a method that corrects for the population structure and genetic relatedness of the various strains. A total of 205 separate genome-wide scans were analyzed. The most significant hits include three candidate loci related to cardiac and body weight, three loci for electrocardiographic (ECG) values, two loci for the susceptibility of atrial weight index to iso, four loci for the susceptibility of systolic blood pressure (SBP) to perturbations of the $beta$-adrenergic system, and one locus for the responsiveness of QTc (p<10(-8)). An additional 60 loci were suggestive for one or the other of the 27 traits, while 46 others were suggestive for one or the other drug effects (p<10(-6)). Most hits tagged unexpected regions, yet at least two loci for the susceptibility of SBP to $beta$-adrenergic drugs pointed at members of the hypothalamic-pituitary-thyroid axis. Loci for cardiac-related traits were preferentially enriched in genes expressed in the heart, while 23% of the testable loci were replicated with datasets of the Mouse Phenome Database (MPD). Altogether these data and validation tests indicate that the mapped loci are relevant to the traits and responses studied. |
Calabrese, Gina; Bennett, Brian J; Orozco, Luz ; Kang, Hyun M; Eskin, Eleazar ; Dombret, Carlos ; Backer, Olivier De ; Lusis, Aldons J; Farber, Charles R Systems genetic analysis of osteoblast-lineage cells. Journal Article PLoS Genet, 8 (12), pp. e1003150, 2012, ISSN: 1553-7404. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Calabrese:PlosGenet:2012, title = {Systems genetic analysis of osteoblast-lineage cells.}, author = { Gina Calabrese and Brian J. Bennett and Luz Orozco and Hyun M. Kang and Eleazar Eskin and Carlos Dombret and Olivier De Backer and Aldons J. Lusis and Charles R. Farber}, url = {http://dx.doi.org/10.1371/journal.pgen.1003150}, issn = {1553-7404}, year = {2012}, date = {2012-01-01}, journal = {PLoS Genet}, volume = {8}, number = {12}, pages = {e1003150}, address = {United States}, organization = {Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America.}, abstract = {The osteoblast-lineage consists of cells at various stages of maturation that are essential for skeletal development, growth, and maintenance. Over the past decade, many of the signaling cascades that regulate this lineage have been elucidated; however, little is known of the networks that coordinate, modulate, and transmit these signals. Here, we identify a gene network specific to the osteoblast-lineage through the reconstruction of a bone co-expression network using microarray profiles collected on 96 Hybrid Mouse Diversity Panel (HMDP) inbred strains. Of the 21 modules that comprised the bone network, module 9 (M9) contained genes that were highly correlated with prototypical osteoblast maker genes and were more highly expressed in osteoblasts relative to other bone cells. In addition, the M9 contained many of the key genes that define the osteoblast-lineage, which together suggested that it was specific to this lineage. To use the M9 to identify novel osteoblast genes and highlight its biological relevance, we knocked-down the expression of its two most connected "hub" genes, Maged1 and Pard6g. Their perturbation altered both osteoblast proliferation and differentiation. Furthermore, we demonstrated the mice deficient in Maged1 had decreased bone mineral density (BMD). It was also discovered that a local expression quantitative trait locus (eQTL) regulating the Wnt signaling antagonist Sfrp1 was a key driver of the M9. We also show that the M9 is associated with BMD in the HMDP and is enriched for genes implicated in the regulation of human BMD through genome-wide association studies. In conclusion, we have identified a physiologically relevant gene network and used it to discover novel genes and regulatory mechanisms involved in the function of osteoblast-lineage cells. Our results highlight the power of harnessing natural genetic variation to generate co-expression networks that can be used to gain insight into the function of specific cell-types}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } The osteoblast-lineage consists of cells at various stages of maturation that are essential for skeletal development, growth, and maintenance. Over the past decade, many of the signaling cascades that regulate this lineage have been elucidated; however, little is known of the networks that coordinate, modulate, and transmit these signals. Here, we identify a gene network specific to the osteoblast-lineage through the reconstruction of a bone co-expression network using microarray profiles collected on 96 Hybrid Mouse Diversity Panel (HMDP) inbred strains. Of the 21 modules that comprised the bone network, module 9 (M9) contained genes that were highly correlated with prototypical osteoblast maker genes and were more highly expressed in osteoblasts relative to other bone cells. In addition, the M9 contained many of the key genes that define the osteoblast-lineage, which together suggested that it was specific to this lineage. To use the M9 to identify novel osteoblast genes and highlight its biological relevance, we knocked-down the expression of its two most connected "hub" genes, Maged1 and Pard6g. Their perturbation altered both osteoblast proliferation and differentiation. Furthermore, we demonstrated the mice deficient in Maged1 had decreased bone mineral density (BMD). It was also discovered that a local expression quantitative trait locus (eQTL) regulating the Wnt signaling antagonist Sfrp1 was a key driver of the M9. We also show that the M9 is associated with BMD in the HMDP and is enriched for genes implicated in the regulation of human BMD through genome-wide association studies. In conclusion, we have identified a physiologically relevant gene network and used it to discover novel genes and regulatory mechanisms involved in the function of osteoblast-lineage cells. Our results highlight the power of harnessing natural genetic variation to generate co-expression networks that can be used to gain insight into the function of specific cell-types |
Furlotte, Nicholas A; Kang, Eun Yong; Nas, Atila Van; Farber, Charles R; Lusis, Aldons J; Eskin, Eleazar Increasing Association Mapping Power and Resolution in Mouse Genetic Studies Through the Use of Meta-analysis for Structured Populations. Journal Article Genetics, 191 (3), pp. 959-67, 2012, ISSN: 1943-2631. Abstract | Links | BibTeX | Tags: Meta-Analysis, Meta-Analysis Grant, Mouse Genetics @article{Furlotte:Genetics:2012, title = {Increasing Association Mapping Power and Resolution in Mouse Genetic Studies Through the Use of Meta-analysis for Structured Populations.}, author = { Nicholas A. Furlotte and Eun Yong Kang and Atila Van Nas and Charles R. Farber and Aldons J. Lusis and Eleazar Eskin}, url = {http://dx.doi.org/10.1534/genetics.112.140277}, issn = {1943-2631}, year = {2012}, date = {2012-01-01}, journal = {Genetics}, volume = {191}, number = {3}, pages = {959-67}, address = {United States}, organization = {University of California, Los Angeles;}, abstract = {Genetic studies in mouse models have played an integral role in the discovery of the mechanisms underlying many human diseases. The primary mode of discovery has been the application of linkage analysis to mouse crosses. This approach results in high power to identify regions that affect traits, but in low resolution, making it difficult to identify the precise genomic location harboring the causal variant. Recently, a panel of mice referred to as the hybrid mouse diversity panel (HMDP) has been developed to overcome this problem. However, power in this panel is limited by the availability of inbred strains. Previous studies have suggested combining results across multiple panels as a means to increase power, but the methods employed may not be well suited for structured populations, such as the HMDP. In this paper, we introduce a meta-analysis based method that may be used to combine HMDP studies with F2 cross studies to gain power, while increasing resolution. Due to the drastically different genetic structure of F2s and the HMDP, the best way to combine two studies for a given SNP depends on the strain distribution pattern in each study. We show that combining results, while accounting for these patterns, leads to increased power and resolution. Using our method to map bone mineral density, we find that two previously implicated loci are replicated with increased significance and that the size of the associated is decreased. We also map HDL cholesterol and show a dramatic increase in the significance of a previously identified result.}, keywords = {Meta-Analysis, Meta-Analysis Grant, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Genetic studies in mouse models have played an integral role in the discovery of the mechanisms underlying many human diseases. The primary mode of discovery has been the application of linkage analysis to mouse crosses. This approach results in high power to identify regions that affect traits, but in low resolution, making it difficult to identify the precise genomic location harboring the causal variant. Recently, a panel of mice referred to as the hybrid mouse diversity panel (HMDP) has been developed to overcome this problem. However, power in this panel is limited by the availability of inbred strains. Previous studies have suggested combining results across multiple panels as a means to increase power, but the methods employed may not be well suited for structured populations, such as the HMDP. In this paper, we introduce a meta-analysis based method that may be used to combine HMDP studies with F2 cross studies to gain power, while increasing resolution. Due to the drastically different genetic structure of F2s and the HMDP, the best way to combine two studies for a given SNP depends on the strain distribution pattern in each study. We show that combining results, while accounting for these patterns, leads to increased power and resolution. Using our method to map bone mineral density, we find that two previously implicated loci are replicated with increased significance and that the size of the associated is decreased. We also map HDL cholesterol and show a dramatic increase in the significance of a previously identified result. |
2011 |
Ghazalpour, Anatole; Bennett, Brian; Petyuk, Vladislav A; Orozco, Luz; Hagopian, Raffi; Mungrue, Imran N; Farber, Charles R; Sinsheimer, Janet; Kang, Hyun M; Furlotte, Nicholas; Park, Christopher C; Wen, Ping-Zi; Brewer, Heather; Weitz, Karl; Camp, David G; Pan, Calvin; Yordanova, Roumyana; Neuhaus, Isaac; Tilford, Charles; Siemers, Nathan; Gargalovic, Peter; Eskin, Eleazar; Kirchgessner, Todd; Smith, Desmond J; Smith, Richard D; Lusis, Aldons J Comparative analysis of proteome and transcriptome variation in mouse. Journal Article PLoS Genet, 7 (6), pp. e1001393, 2011, ISSN: 1553-7404. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Ghazalpour:PlosGenet:2011, title = {Comparative analysis of proteome and transcriptome variation in mouse.}, author = {Anatole Ghazalpour and Brian Bennett and Vladislav A Petyuk and Luz Orozco and Raffi Hagopian and Imran N Mungrue and Charles R Farber and Janet Sinsheimer and Hyun M Kang and Nicholas Furlotte and Christopher C Park and Ping-Zi Wen and Heather Brewer and Karl Weitz and David G Camp and Calvin Pan and Roumyana Yordanova and Isaac Neuhaus and Charles Tilford and Nathan Siemers and Peter Gargalovic and Eleazar Eskin and Todd Kirchgessner and Desmond J Smith and Richard D Smith and Aldons J Lusis}, url = {http://dx.doi.org/10.1371/journal.pgen.1001393}, issn = {1553-7404}, year = {2011}, date = {2011-01-01}, journal = {PLoS Genet}, volume = {7}, number = {6}, pages = {e1001393}, address = {United States}, organization = {Department of Medicine/Division of Cardiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America.}, abstract = {The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications. |
Park, Christopher C; Gale, Greg D; de Jong, Simone ; Ghazalpour, Anatole ; Bennett, Brian J; Farber, Charles R; Langfelder, Peter ; Lin, Andy ; Khan, Arshad H; Eskin, Eleazar ; Horvath, Steve ; Lusis, Aldons J; Ophoff, Roel A; Smith, Desmond J Gene networks associated with conditional fear in mice identified using a systems genetics approach. Journal Article BMC Syst Biol, 5 , pp. 43, 2011, ISSN: 1752-0509. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Park:BmcSystBiol:2011, title = {Gene networks associated with conditional fear in mice identified using a systems genetics approach.}, author = { Christopher C. Park and Greg D. Gale and Simone de Jong and Anatole Ghazalpour and Brian J. Bennett and Charles R. Farber and Peter Langfelder and Andy Lin and Arshad H. Khan and Eleazar Eskin and Steve Horvath and Aldons J. Lusis and Roel A. Ophoff and Desmond J. Smith}, url = {http://dx.doi.org/10.1186/1752-0509-5-43}, issn = {1752-0509}, year = {2011}, date = {2011-01-01}, journal = {BMC Syst Biol}, volume = {5}, pages = {43}, address = {England}, organization = {Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA. DSmith@mednet.ucla.edu.}, abstract = {UNLABELLED: ABSTRACT: BACKGROUND: Our understanding of the genetic basis of learning and memory remains shrouded in mystery. To explore the genetic networks governing the biology of conditional fear, we used a systems genetics approach to analyze a hybrid mouse diversity panel (HMDP) with high mapping resolution. RESULTS: A total of 27 behavioral quantitative trait loci were mapped with a false discovery rate of 5%. By integrating fear phenotypes, transcript profiling data from hippocampus and striatum and also genotype information, two gene co-expression networks correlated with context-dependent immobility were identified. We prioritized the key markers and genes in these pathways using intramodular connectivity measures and structural equation modeling. Highly connected genes in the context fear modules included Psmd6, Ube2a and Usp33, suggesting an important role for ubiquitination in learning and memory. In addition, we surveyed the architecture of brain transcript regulation and demonstrated preservation of gene co-expression modules in hippocampus and striatum, while also highlighting important differences. Rps15a, Kif3a, Stard7, 6330503K22RIK, and Plvap were among the individual genes whose transcript abundance were strongly associated with fear phenotypes. CONCLUSION: Application of our multi-faceted mapping strategy permits an increasingly detailed characterization of the genetic networks underlying behavior.}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } UNLABELLED: ABSTRACT: BACKGROUND: Our understanding of the genetic basis of learning and memory remains shrouded in mystery. To explore the genetic networks governing the biology of conditional fear, we used a systems genetics approach to analyze a hybrid mouse diversity panel (HMDP) with high mapping resolution. RESULTS: A total of 27 behavioral quantitative trait loci were mapped with a false discovery rate of 5%. By integrating fear phenotypes, transcript profiling data from hippocampus and striatum and also genotype information, two gene co-expression networks correlated with context-dependent immobility were identified. We prioritized the key markers and genes in these pathways using intramodular connectivity measures and structural equation modeling. Highly connected genes in the context fear modules included Psmd6, Ube2a and Usp33, suggesting an important role for ubiquitination in learning and memory. In addition, we surveyed the architecture of brain transcript regulation and demonstrated preservation of gene co-expression modules in hippocampus and striatum, while also highlighting important differences. Rps15a, Kif3a, Stard7, 6330503K22RIK, and Plvap were among the individual genes whose transcript abundance were strongly associated with fear phenotypes. CONCLUSION: Application of our multi-faceted mapping strategy permits an increasingly detailed characterization of the genetic networks underlying behavior. |
Farber, Charles R; Bennett, Brian J; Orozco, Luz; Zou, Wei; Lira, Ana; Kostem, Emrah; Kang, Hyun Min; Furlotte, Nicholas; Berberyan, Ani; Ghazalpour, Anatole; Suwanwela, Jaijam; Drake, Thomas A; Eskin, Eleazar; Wang, Tian Q; Teitelbaum, Steven L; Lusis, Aldons J Mouse genome-wide association and systems genetics identify asxl2 as a regulator of bone mineral density and osteoclastogenesis. Journal Article PLoS Genet, 7 (4), pp. e1002038, 2011, ISSN: 1553-7404. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Farber:PlosGenet:2011, title = {Mouse genome-wide association and systems genetics identify asxl2 as a regulator of bone mineral density and osteoclastogenesis.}, author = {Charles R Farber and Brian J Bennett and Luz Orozco and Wei Zou and Ana Lira and Emrah Kostem and Hyun Min Kang and Nicholas Furlotte and Ani Berberyan and Anatole Ghazalpour and Jaijam Suwanwela and Thomas A Drake and Eleazar Eskin and Q Tian Wang and Steven L Teitelbaum and Aldons J Lusis}, url = {http://dx.doi.org/10.1371/journal.pgen.1002038}, issn = {1553-7404}, year = {2011}, date = {2011-01-01}, journal = {PLoS Genet}, volume = {7}, number = {4}, pages = {e1002038}, address = {United States}, organization = {Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America.}, abstract = {Significant advances have been made in the discovery of genes affecting bone mineral density (BMD); however, our understanding of its genetic basis remains incomplete. In the current study, genome-wide association (GWA) and co-expression network analysis were used in the recently described Hybrid Mouse Diversity Panel (HMDP) to identify and functionally characterize novel BMD genes. In the HMDP, a GWA of total body, spinal, and femoral BMD revealed four significant associations (-log10P>5.39) affecting at least one BMD trait on chromosomes (Chrs.) 7, 11, 12, and 17. The associations implicated a total of 163 genes with each association harboring between 14 and 112 genes. This list was reduced to 26 functional candidates by identifying those genes that were regulated by local eQTL in bone or harbored potentially functional non-synonymous (NS) SNPs. This analysis revealed that the most significant BMD SNP on Chr. 12 was a NS SNP in the additional sex combs like-2 (Asxl2) gene that was predicted to be functional. The involvement of Asxl2 in the regulation of bone mass was confirmed by the observation that Asxl2 knockout mice had reduced BMD. To begin to unravel the mechanism through which Asxl2 influenced BMD, a gene co-expression network was created using cortical bone gene expression microarray data from the HMDP strains. Asxl2 was identified as a member of a co-expression module enriched for genes involved in the differentiation of myeloid cells. In bone, osteoclasts are bone-resorbing cells of myeloid origin, suggesting that Asxl2 may play a role in osteoclast differentiation. In agreement, the knockdown of Asxl2 in bone marrow macrophages impaired their ability to form osteoclasts. This study identifies a new regulator of BMD and osteoclastogenesis and highlights the power of GWA and systems genetics in the mouse for dissecting complex genetic traits.}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Significant advances have been made in the discovery of genes affecting bone mineral density (BMD); however, our understanding of its genetic basis remains incomplete. In the current study, genome-wide association (GWA) and co-expression network analysis were used in the recently described Hybrid Mouse Diversity Panel (HMDP) to identify and functionally characterize novel BMD genes. In the HMDP, a GWA of total body, spinal, and femoral BMD revealed four significant associations (-log10P>5.39) affecting at least one BMD trait on chromosomes (Chrs.) 7, 11, 12, and 17. The associations implicated a total of 163 genes with each association harboring between 14 and 112 genes. This list was reduced to 26 functional candidates by identifying those genes that were regulated by local eQTL in bone or harbored potentially functional non-synonymous (NS) SNPs. This analysis revealed that the most significant BMD SNP on Chr. 12 was a NS SNP in the additional sex combs like-2 (Asxl2) gene that was predicted to be functional. The involvement of Asxl2 in the regulation of bone mass was confirmed by the observation that Asxl2 knockout mice had reduced BMD. To begin to unravel the mechanism through which Asxl2 influenced BMD, a gene co-expression network was created using cortical bone gene expression microarray data from the HMDP strains. Asxl2 was identified as a member of a co-expression module enriched for genes involved in the differentiation of myeloid cells. In bone, osteoclasts are bone-resorbing cells of myeloid origin, suggesting that Asxl2 may play a role in osteoclast differentiation. In agreement, the knockdown of Asxl2 in bone marrow macrophages impaired their ability to form osteoclasts. This study identifies a new regulator of BMD and osteoclastogenesis and highlights the power of GWA and systems genetics in the mouse for dissecting complex genetic traits. |
Keane, Thomas M; Goodstadt, Leo; Danecek, Petr; White, Michael A; Wong, Kim; Yalcin, Binnaz; Heger, Andreas; Agam, Avigail; Slater, Guy; Goodson, Martin; Furlotte, Nicholas A; Eskin, Eleazar; Nellåker, Christoffer; Whitley, Helen; Cleak, James; Janowitz, Deborah; Hernandez-Pliego, Polinka; Edwards, Andrew; Belgard, Grant T; Oliver, Peter L; McIntyre, Rebecca E; Bhomra, Amarjit; Nicod, Jérôme; Gan, Xiangchao; Yuan, Wei; van der Weyden, Louise; Steward, Charles A; Bala, Sendu; Stalker, Jim; Mott, Richard; Durbin, Richard Mouse genomic variation and its effect on phenotypes and gene regulation. Journal Article Nature, 477 (7364), pp. 289-94, 2011, ISSN: 1476-4687. Abstract | Links | BibTeX | Tags: Mouse Genetics @article{Keane:Nature:2011, title = {Mouse genomic variation and its effect on phenotypes and gene regulation.}, author = {Thomas M Keane and Leo Goodstadt and Petr Danecek and Michael A White and Kim Wong and Binnaz Yalcin and Andreas Heger and Avigail Agam and Guy Slater and Martin Goodson and Nicholas A Furlotte and Eleazar Eskin and Christoffer Nellåker and Helen Whitley and James Cleak and Deborah Janowitz and Polinka Hernandez-Pliego and Andrew Edwards and T Grant Belgard and Peter L Oliver and Rebecca E McIntyre and Amarjit Bhomra and Jérôme Nicod and Xiangchao Gan and Wei Yuan and Louise van der Weyden and Charles A Steward and Sendu Bala and Jim Stalker and Richard Mott and Richard Durbin}, url = {http://dx.doi.org/10.1038/nature10413}, issn = {1476-4687}, year = {2011}, date = {2011-01-01}, journal = {Nature}, volume = {477}, number = {7364}, pages = {289-94}, address = {England}, organization = {The Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1HH, UK.}, abstract = {We report genome sequences of 17 inbred strains of laboratory mice and identify almost ten times more variants than previously known. We use these genomes to explore the phylogenetic history of the laboratory mouse and to examine the functional consequences of allele-specific variation on transcript abundance, revealing that at least 12% of transcripts show a significant tissue-specific expression bias. By identifying candidate functional variants at 718 quantitative trait loci we show that the molecular nature of functional variants and their position relative to genes vary according to the effect size of the locus. These sequences provide a starting point for a new era in the functional analysis of a key model organism.}, keywords = {Mouse Genetics}, pubstate = {published}, tppubtype = {article} } We report genome sequences of 17 inbred strains of laboratory mice and identify almost ten times more variants than previously known. We use these genomes to explore the phylogenetic history of the laboratory mouse and to examine the functional consequences of allele-specific variation on transcript abundance, revealing that at least 12% of transcripts show a significant tissue-specific expression bias. By identifying candidate functional variants at 718 quantitative trait loci we show that the molecular nature of functional variants and their position relative to genes vary according to the effect size of the locus. These sequences provide a starting point for a new era in the functional analysis of a key model organism. |
2010 |
Bennett, Brian J; Farber, Charles R; Orozco, Luz; Kang, Hyun Min; Ghazalpour, Anatole; Siemers, Nathan; Neubauer, Michael; Neuhaus, Isaac; Yordanova, Roumyana; Guan, Bo; Truong, Amy; Yang, Wen-Pin; He, Aiqing; Kayne, Paul; Gargalovic, Peter; Kirchgessner, Todd; Pan, Calvin; Castellani, Lawrence W; Kostem, Emrah; Furlotte, Nicholas; Drake, Thomas A; Eskin, Eleazar; Lusis, Aldons J A high-resolution association mapping panel for the dissection of complex traits in mice. Journal Article Genome Res, 20 (2), pp. 281-90, 2010, ISSN: 1549-5469. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Bennett:GenomeRes:2010, title = {A high-resolution association mapping panel for the dissection of complex traits in mice.}, author = {Brian J Bennett and Charles R Farber and Luz Orozco and Hyun Min Kang and Anatole Ghazalpour and Nathan Siemers and Michael Neubauer and Isaac Neuhaus and Roumyana Yordanova and Bo Guan and Amy Truong and Wen-Pin Yang and Aiqing He and Paul Kayne and Peter Gargalovic and Todd Kirchgessner and Calvin Pan and Lawrence W Castellani and Emrah Kostem and Nicholas Furlotte and Thomas A Drake and Eleazar Eskin and Aldons J Lusis}, url = {http://dx.doi.org/10.1101/gr.099234.109}, issn = {1549-5469}, year = {2010}, date = {2010-01-01}, journal = {Genome Res}, volume = {20}, number = {2}, pages = {281-90}, address = {United States}, organization = {Department of Medicine/Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles California 90095, USA;}, abstract = {Systems genetics relies on common genetic variants to elucidate biologic networks contributing to complex disease-related phenotypes. Mice are ideal model organisms for such approaches, but linkage analysis has been only modestly successful due to low mapping resolution. Association analysis in mice has the potential of much better resolution, but it is confounded by population structure and inadequate power to map traits that explain less than 10% of the variance, typical of mouse quantitative trait loci (QTL). We report a novel strategy for association mapping that combines classic inbred strains for mapping resolution and recombinant inbred strains for mapping power. Using a mixed model algorithm to correct for population structure, we validate the approach by mapping over 2500 cis-expression QTL with a resolution an order of magnitude narrower than traditional QTL analysis. We also report the fine mapping of metabolic traits such as plasma lipids. This resource, termed the Hybrid Mouse Diversity Panel, makes possible the integration of multiple data sets and should prove useful for systems-based approaches to complex traits and studies of gene-by-environment interactions.}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Systems genetics relies on common genetic variants to elucidate biologic networks contributing to complex disease-related phenotypes. Mice are ideal model organisms for such approaches, but linkage analysis has been only modestly successful due to low mapping resolution. Association analysis in mice has the potential of much better resolution, but it is confounded by population structure and inadequate power to map traits that explain less than 10% of the variance, typical of mouse quantitative trait loci (QTL). We report a novel strategy for association mapping that combines classic inbred strains for mapping resolution and recombinant inbred strains for mapping power. Using a mixed model algorithm to correct for population structure, we validate the approach by mapping over 2500 cis-expression QTL with a resolution an order of magnitude narrower than traditional QTL analysis. We also report the fine mapping of metabolic traits such as plasma lipids. This resource, termed the Hybrid Mouse Diversity Panel, makes possible the integration of multiple data sets and should prove useful for systems-based approaches to complex traits and studies of gene-by-environment interactions. |
Kang, Hyun Min; Zaitlen, Noah A; Eskin, Eleazar EMINIM: an adaptive and memory-efficient algorithm for genotype imputation. Journal Article J Comput Biol, 17 (3), pp. 547-60, 2010, ISSN: 1557-8666. Abstract | Links | BibTeX | Tags: Imputation, Mouse Genetics @article{Kang:JComputBiol:2010a, title = {EMINIM: an adaptive and memory-efficient algorithm for genotype imputation.}, author = { Hyun Min Kang and Noah A. Zaitlen and Eleazar Eskin}, url = {http://dx.doi.org/10.1089/cmb.2009.0199}, issn = {1557-8666}, year = {2010}, date = {2010-01-01}, journal = {J Comput Biol}, volume = {17}, number = {3}, pages = {547-60}, address = {United States}, organization = {Biostatistics Department, University of Michigan, Ann Arbor, Ann Arbor, Michigan, USA.}, abstract = {Genome-wide association studies have proven to be a highly successful method for identification of genetic loci for complex phenotypes in both humans and model organisms. These large scale studies rely on the collection of hundreds of thousands of single nucleotide polymorphisms (SNPs) across the genome. Standard high-throughput genotyping technologies capture only a fraction of the total genetic variation. Recent efforts have shown that it is possible to "impute" with high accuracy the genotypes of SNPs that are not collected in the study provided that they are present in a reference data set which contains both SNPs collected in the study as well as other SNPs. We here introduce a novel HMM based technique to solve the imputation problem that addresses several shortcomings of existing methods. First, our method is adaptive which lets it estimate population genetic parameters from the data and be applied to model organisms that have very different evolutionary histories. Compared to previous methods, our method is up to ten times more accurate on model organisms such as mouse. Second, our algorithm scales in memory usage in the number of collected markers as opposed to the number of known SNPs. This issue is very relevant due to the size of the reference data sets currently being generated. We compare our method over mouse and human data sets to existing methods, and show that each has either comparable or better performance and much lower memory usage. The method is available for download at http://genetics.cs.ucla.edu/eminim.}, keywords = {Imputation, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Genome-wide association studies have proven to be a highly successful method for identification of genetic loci for complex phenotypes in both humans and model organisms. These large scale studies rely on the collection of hundreds of thousands of single nucleotide polymorphisms (SNPs) across the genome. Standard high-throughput genotyping technologies capture only a fraction of the total genetic variation. Recent efforts have shown that it is possible to "impute" with high accuracy the genotypes of SNPs that are not collected in the study provided that they are present in a reference data set which contains both SNPs collected in the study as well as other SNPs. We here introduce a novel HMM based technique to solve the imputation problem that addresses several shortcomings of existing methods. First, our method is adaptive which lets it estimate population genetic parameters from the data and be applied to model organisms that have very different evolutionary histories. Compared to previous methods, our method is up to ten times more accurate on model organisms such as mouse. Second, our algorithm scales in memory usage in the number of collected markers as opposed to the number of known SNPs. This issue is very relevant due to the size of the reference data sets currently being generated. We compare our method over mouse and human data sets to existing methods, and show that each has either comparable or better performance and much lower memory usage. The method is available for download at http://genetics.cs.ucla.edu/eminim. |
Kirby, Andrew; Kang, Hyun Min; Wade, Claire M; Cotsapas, Chris J; Kostem, Emrah; Han, Buhm; Furlotte, Nick; Kang, Eun Yong; Rivas, Manuel; Bogue, Molly A; Frazer, Kelly A; Johnson, Frank M; Beilharz, Erica J; Cox, David R; Eskin, Eleazar; Daly, Mark J Fine Mapping in 94 Inbred Mouse Strains Using a High-density Haplotype Resource. Journal Article Genetics, 2010, ISSN: 1943-2631. Abstract | Links | BibTeX | Tags: Mouse Genetics @article{Kirby:Genetics:2010, title = {Fine Mapping in 94 Inbred Mouse Strains Using a High-density Haplotype Resource.}, author = {Andrew Kirby and Hyun Min Kang and Claire M Wade and Chris J Cotsapas and Emrah Kostem and Buhm Han and Nick Furlotte and Eun Yong Kang and Manuel Rivas and Molly A Bogue and Kelly A Frazer and Frank M Johnson and Erica J Beilharz and David R Cox and Eleazar Eskin and Mark J Daly}, url = {http://dx.doi.org/10.1534/genetics.110.115014}, issn = {1943-2631}, year = {2010}, date = {2010-01-01}, journal = {Genetics}, organization = {Massachusetts General Hospital.}, abstract = {The genetics of phenotypic variation in inbred mice has for nearly a century provided a primary weapon in the medical research arsenal. A catalogue of the genetic variation among inbred mouse strains, however, is required to enable powerful positional cloning and association techniques. A recent whole genome resequencing study of 15 inbred mouse strains captured a significant fraction of the genetic variation among a limited number of strains (FRAZER et al., 2007) - yet the common use of hundreds of inbred strains in medical research motivates the need for a high-density variation map of a larger set of strains. Here we report a dense set of genotypes from 94 inbred mouse strains containing 10.77 million genotypes over 121,433 single nucleotide polymorphisms (SNPs), dispersed at 20 kb intervals on average across the genome, with an average concordance of 99.94% with previous SNP sets. Through pairwise comparisons of the strains, we identified an average of 4.70 distinct segments over 73 classical inbred strains in each region of the genome, suggesting limited genetic diversity between the strains. Combining these data with genotypes of 7,570 gap-filling SNPs, we further imputed the untyped or missing genotypes of 94 strains over 8.27 million Perlegen SNPs. The imputation accuracy among classical inbred strains is estimated at 99.7% for the genotypes imputed with high confidence. We demonstrated the utility of this data in highresolution linkage mapping through power simulations and statistical power analysis and provide guidelines for developing such studies. We also provide a resource of in-silico association mapping between the complex traits deposited to Mouse Phenome Database (MPD) with our genotypes. We expect that these resources will facilitate effective designs of both human and mouse studies for dissecting genetic basis of complex traits.}, keywords = {Mouse Genetics}, pubstate = {published}, tppubtype = {article} } The genetics of phenotypic variation in inbred mice has for nearly a century provided a primary weapon in the medical research arsenal. A catalogue of the genetic variation among inbred mouse strains, however, is required to enable powerful positional cloning and association techniques. A recent whole genome resequencing study of 15 inbred mouse strains captured a significant fraction of the genetic variation among a limited number of strains (FRAZER et al., 2007) - yet the common use of hundreds of inbred strains in medical research motivates the need for a high-density variation map of a larger set of strains. Here we report a dense set of genotypes from 94 inbred mouse strains containing 10.77 million genotypes over 121,433 single nucleotide polymorphisms (SNPs), dispersed at 20 kb intervals on average across the genome, with an average concordance of 99.94% with previous SNP sets. Through pairwise comparisons of the strains, we identified an average of 4.70 distinct segments over 73 classical inbred strains in each region of the genome, suggesting limited genetic diversity between the strains. Combining these data with genotypes of 7,570 gap-filling SNPs, we further imputed the untyped or missing genotypes of 94 strains over 8.27 million Perlegen SNPs. The imputation accuracy among classical inbred strains is estimated at 99.7% for the genotypes imputed with high confidence. We demonstrated the utility of this data in highresolution linkage mapping through power simulations and statistical power analysis and provide guidelines for developing such studies. We also provide a resource of in-silico association mapping between the complex traits deposited to Mouse Phenome Database (MPD) with our genotypes. We expect that these resources will facilitate effective designs of both human and mouse studies for dissecting genetic basis of complex traits. |
McLachlan, Stela; Lee, Seung-Min M; Steele, Teresa M; Hawthorne, Paula L; Zapala, Matthew A; Eskin, Eleazar ; Schork, Nicholas J; Anderson, Gregory J; Vulpe, Chris D In silico QTL mapping of basal liver iron levels in inbred mouse strains. Journal Article Physiol Genomics, 2010, ISSN: 1531-2267. Abstract | Links | BibTeX | Tags: Mouse Genetics @article{McLachlan:PhysiolGenomics:2010, title = {In silico QTL mapping of basal liver iron levels in inbred mouse strains.}, author = { Stela McLachlan and Seung-Min M. Lee and Teresa M. Steele and Paula L. Hawthorne and Matthew A. Zapala and Eleazar Eskin and Nicholas J. Schork and Gregory J. Anderson and Chris D. Vulpe}, url = {http://dx.doi.org/10.1152/physiolgenomics.00025.2010}, issn = {1531-2267}, year = {2010}, date = {2010-01-01}, journal = {Physiol Genomics}, organization = {University of California Berkeley.}, abstract = {Both iron deficiency and iron excess are detrimental in many organisms, and previous studies in both mice and humans suggest that genetic variation may influence iron status in mammals. However, these genetic factors are not well defined. To address this issue, we measured basal liver iron levels in 18 inbred strains of mice of both sexes on a defined iron diet and found ~4 fold variation in liver iron in males (lowest 153g/g, highest 661g/g) and ~3 fold variation in females (lowest 222g/g, highest 658g/g). We carried out a genome-wide association mapping to identify haplotypes underlying differences in liver iron and three other related traits (copper and zinc liver levels, and plasma diferric transferrin levels) in a subset of 14 inbred strains for which genotype information was available. We identified two putative Quantitative Trait Loci (QTL) that contain genes with a known role in iron metabolism: Eif2ak1 and Igf2r. We also identified four putative QTL which reside in previously identified iron-related QTL and 22 novel putative QTL. The most promising putative QTL include a 0.22 Mb region on Chromosome 7 and a 0.32 Mb region on Chromosome 11 which both contain only one candidate gene, Adam12 and Gria1, respectively. Identified putative QTL are good candidates for further refinement and subsequent functional studies.}, keywords = {Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Both iron deficiency and iron excess are detrimental in many organisms, and previous studies in both mice and humans suggest that genetic variation may influence iron status in mammals. However, these genetic factors are not well defined. To address this issue, we measured basal liver iron levels in 18 inbred strains of mice of both sexes on a defined iron diet and found ~4 fold variation in liver iron in males (lowest 153g/g, highest 661g/g) and ~3 fold variation in females (lowest 222g/g, highest 658g/g). We carried out a genome-wide association mapping to identify haplotypes underlying differences in liver iron and three other related traits (copper and zinc liver levels, and plasma diferric transferrin levels) in a subset of 14 inbred strains for which genotype information was available. We identified two putative Quantitative Trait Loci (QTL) that contain genes with a known role in iron metabolism: Eif2ak1 and Igf2r. We also identified four putative QTL which reside in previously identified iron-related QTL and 22 novel putative QTL. The most promising putative QTL include a 0.22 Mb region on Chromosome 7 and a 0.32 Mb region on Chromosome 11 which both contain only one candidate gene, Adam12 and Gria1, respectively. Identified putative QTL are good candidates for further refinement and subsequent functional studies. |
2009 |
Yang, Ivana V; Wade, Claire M; Kang, Hyun Min ; Alper, Scott ; Rutledge, Holly ; Lackford, Brad ; Eskin, Eleazar ; Daly, Mark J; Schwartz, David A Identification of novel genes that mediate innate immunity using inbred mice. Journal Article Genetics, 183 (4), pp. 1535-44, 2009, ISSN: 1943-2631. Abstract | Links | BibTeX | Tags: Mouse Genetics @article{Yang:Genetics:2009, title = {Identification of novel genes that mediate innate immunity using inbred mice.}, author = { Ivana V. Yang and Claire M. Wade and Hyun Min Kang and Scott Alper and Holly Rutledge and Brad Lackford and Eleazar Eskin and Mark J. Daly and David A. Schwartz}, url = {http://dx.doi.org/10.1534/genetics.109.107540}, issn = {1943-2631}, year = {2009}, date = {2009-01-01}, journal = {Genetics}, volume = {183}, number = {4}, pages = {1535-44}, address = {United States}, organization = {Department of Medicine, National Jewish Health, Denver, Colorado 80206, USA. yangi@njhealth.org}, abstract = {Innate immunity is the first line of defense against microbial infections. Although polymorphisms in toll-like receptors (TLRs) and downstream signaling molecules (CD14, TLR2, TLR4, TLR5, and IRAK4) affect the innate immune response, these variants account for only a portion of the ability of the host to respond to bacteria, fungi, and viruses. To identify other genes involved in the innate immune response, we challenged 16 inbred murine strains with lipopolysaccharide (LPS) systemically and measured serum concentrations of pro-inflammatory cytokines IL-1beta, IL-6, and TNFalpha, and the chemokine KC 6 hr post-treatment. Loci that segregate with strain phenotypes were identified by whole genome association (WGA) mapping of cytokine concentrations. Published gene expression profiles and quantitative trait loci (QTL) were then utilized to prioritize loci and genes that potentially regulate the host response to LPS. Sixteen loci were selected for further investigation by combining WGA analysis with previously published QTL for murine response to LPS or gram negative bacteria. Thirty-eight genes within these loci were then selected for further investigation on the basis of the significance of the identified locus, transcriptional response to LPS, and biological plausibility. RNA interference-mediated inhibition of 4 of 38 candidate genes was shown to block the production of IL-6 in J774A.1 macrophages. In summary, our analysis identified 4 genes that have not previously been implicated in innate immunity, namely, 1110058L19Rik, 4933415F23Rik, Fbxo9, and Ipo7. These genes could represent potential sepsis biomarkers or therapeutic targets that should be further investigated in human populations.}, keywords = {Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Innate immunity is the first line of defense against microbial infections. Although polymorphisms in toll-like receptors (TLRs) and downstream signaling molecules (CD14, TLR2, TLR4, TLR5, and IRAK4) affect the innate immune response, these variants account for only a portion of the ability of the host to respond to bacteria, fungi, and viruses. To identify other genes involved in the innate immune response, we challenged 16 inbred murine strains with lipopolysaccharide (LPS) systemically and measured serum concentrations of pro-inflammatory cytokines IL-1beta, IL-6, and TNFalpha, and the chemokine KC 6 hr post-treatment. Loci that segregate with strain phenotypes were identified by whole genome association (WGA) mapping of cytokine concentrations. Published gene expression profiles and quantitative trait loci (QTL) were then utilized to prioritize loci and genes that potentially regulate the host response to LPS. Sixteen loci were selected for further investigation by combining WGA analysis with previously published QTL for murine response to LPS or gram negative bacteria. Thirty-eight genes within these loci were then selected for further investigation on the basis of the significance of the identified locus, transcriptional response to LPS, and biological plausibility. RNA interference-mediated inhibition of 4 of 38 candidate genes was shown to block the production of IL-6 in J774A.1 macrophages. In summary, our analysis identified 4 genes that have not previously been implicated in innate immunity, namely, 1110058L19Rik, 4933415F23Rik, Fbxo9, and Ipo7. These genes could represent potential sepsis biomarkers or therapeutic targets that should be further investigated in human populations. |
2008 |
Kang, Hyun Min; Zaitlen, Noah A; Wade, Claire M; Kirby, Andrew ; Heckerman, David ; Daly, Mark J; Eskin, Eleazar Efficient control of population structure in model organism association mapping. Journal Article Genetics, 178 (3), pp. 1709-23, 2008, ISSN: 0016-6731. Abstract | Links | BibTeX | Tags: Mouse Genetics, Population Structure Methods @article{Kang:Genetics:2008, title = {Efficient control of population structure in model organism association mapping.}, author = { Hyun Min Kang and Noah A. Zaitlen and Claire M. Wade and Andrew Kirby and David Heckerman and Mark J. Daly and Eleazar Eskin}, url = {http://dx.doi.org/10.1534/genetics.107.080101}, issn = {0016-6731}, year = {2008}, date = {2008-01-01}, journal = {Genetics}, volume = {178}, number = {3}, pages = {1709-23}, address = {United States}, organization = {Department of Computer Science, University of California, Los Angeles, California 90095-1596, USA.}, abstract = {Genomewide association mapping in model organisms such as inbred mouse strains is a promising approach for the identification of risk factors related to human diseases. However, genetic association studies in inbred model organisms are confronted by the problem of complex population structure among strains. This induces inflated false positive rates, which cannot be corrected using standard approaches applied in human association studies such as genomic control or structured association. Recent studies demonstrated that mixed models successfully correct for the genetic relatedness in association mapping in maize and Arabidopsis panel data sets. However, the currently available mixed-model methods suffer from computational inefficiency. In this article, we propose a new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping. Our method takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows us to substantially increase the computational speed and reliability of the results. We applied EMMA to in silico whole-genome association mapping of inbred mouse strains involving hundreds of thousands of SNPs, in addition to Arabidopsis and maize data sets. We also performed extensive simulation studies to estimate the statistical power of EMMA under various SNP effects, varying degrees of population structure, and differing numbers of multiple measurements per strain. Despite the limited power of inbred mouse association mapping due to the limited number of available inbred strains, we are able to identify significantly associated SNPs, which fall into known QTL or genes identified through previous studies while avoiding an inflation of false positives. An R package implementation and webserver of our EMMA method are publicly available.}, keywords = {Mouse Genetics, Population Structure Methods}, pubstate = {published}, tppubtype = {article} } Genomewide association mapping in model organisms such as inbred mouse strains is a promising approach for the identification of risk factors related to human diseases. However, genetic association studies in inbred model organisms are confronted by the problem of complex population structure among strains. This induces inflated false positive rates, which cannot be corrected using standard approaches applied in human association studies such as genomic control or structured association. Recent studies demonstrated that mixed models successfully correct for the genetic relatedness in association mapping in maize and Arabidopsis panel data sets. However, the currently available mixed-model methods suffer from computational inefficiency. In this article, we propose a new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping. Our method takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows us to substantially increase the computational speed and reliability of the results. We applied EMMA to in silico whole-genome association mapping of inbred mouse strains involving hundreds of thousands of SNPs, in addition to Arabidopsis and maize data sets. We also performed extensive simulation studies to estimate the statistical power of EMMA under various SNP effects, varying degrees of population structure, and differing numbers of multiple measurements per strain. Despite the limited power of inbred mouse association mapping due to the limited number of available inbred strains, we are able to identify significantly associated SNPs, which fall into known QTL or genes identified through previous studies while avoiding an inflation of false positives. An R package implementation and webserver of our EMMA method are publicly available. |
Ghazalpour, Anatole; Doss, Sudheer ; Kang, Hyun ; Farber, Charles ; Wen, Ping-Zi Z; Brozell, Alec ; Castellanos, Ruth ; Eskin, Eleazar ; Smith, Desmond J; Drake, Thomas A; Lusis, Aldons J High-resolution mapping of gene expression using association in an outbred mouse stock. Journal Article PLoS Genet, 4 (8), pp. e1000149, 2008, ISSN: 1553-7404. Abstract | Links | BibTeX | Tags: Mouse Genetics @article{Ghazalpour:PlosGenet:2008, title = {High-resolution mapping of gene expression using association in an outbred mouse stock.}, author = { Anatole Ghazalpour and Sudheer Doss and Hyun Kang and Charles Farber and Ping-Zi Z. Wen and Alec Brozell and Ruth Castellanos and Eleazar Eskin and Desmond J. Smith and Thomas A. Drake and Aldons J. Lusis}, url = {http://dx.doi.org/10.1371/journal.pgen.1000149}, issn = {1553-7404}, year = {2008}, date = {2008-01-01}, journal = {PLoS Genet}, volume = {4}, number = {8}, pages = {e1000149}, address = {United States}, organization = {Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America. aghazalp@ucla.edu}, abstract = {Quantitative trait locus (QTL) analysis is a powerful tool for mapping genes for complex traits in mice, but its utility is limited by poor resolution. A promising mapping approach is association analysis in outbred stocks or different inbred strains. As a proof of concept for the association approach, we applied whole-genome association analysis to hepatic gene expression traits in an outbred mouse population, the MF1 stock, and replicated expression QTL (eQTL) identified in previous studies of F2 intercross mice. We found that the mapping resolution of these eQTL was significantly greater in the outbred population. Through an example, we also showed how this precise mapping can be used to resolve previously identified loci (in intercross studies), which affect many different transcript levels (known as eQTL "hotspots"), into distinct regions. Our results also highlight the importance of correcting for population structure in whole-genome association studies in the outbred stock.}, keywords = {Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Quantitative trait locus (QTL) analysis is a powerful tool for mapping genes for complex traits in mice, but its utility is limited by poor resolution. A promising mapping approach is association analysis in outbred stocks or different inbred strains. As a proof of concept for the association approach, we applied whole-genome association analysis to hepatic gene expression traits in an outbred mouse population, the MF1 stock, and replicated expression QTL (eQTL) identified in previous studies of F2 intercross mice. We found that the mapping resolution of these eQTL was significantly greater in the outbred population. Through an example, we also showed how this precise mapping can be used to resolve previously identified loci (in intercross studies), which affect many different transcript levels (known as eQTL "hotspots"), into distinct regions. Our results also highlight the importance of correcting for population structure in whole-genome association studies in the outbred stock. |
2007 |
Frazer, Kelly A; Eskin, Eleazar ; Kang, Hyun Min ; Bogue, Molly A; Hinds, David A; Beilharz, Erica J; Gupta, Robert V; Montgomery, Julie ; Morenzoni, Matt M; Nilsen, Geoffrey B; Pethiyagoda, Charit L; Stuve, Laura L; Johnson, Frank M; Daly, Mark J; Wade, Claire M; Cox, David R A sequence-based variation map of 8.27 million SNPs in inbred mouse strains. Journal Article Nature, 448 (7157), pp. 1050-3, 2007, ISSN: 1476-4687. Abstract | Links | BibTeX | Tags: Mouse Genetics @article{Frazer:Nature:2007, title = {A sequence-based variation map of 8.27 million SNPs in inbred mouse strains.}, author = { Kelly A. Frazer and Eleazar Eskin and Hyun Min Kang and Molly A. Bogue and David A. Hinds and Erica J. Beilharz and Robert V. Gupta and Julie Montgomery and Matt M. Morenzoni and Geoffrey B. Nilsen and Charit L. Pethiyagoda and Laura L. Stuve and Frank M. Johnson and Mark J. Daly and Claire M. Wade and David R. Cox}, url = {http://dx.doi.org/10.1038/nature06067}, issn = {1476-4687}, year = {2007}, date = {2007-01-01}, journal = {Nature}, volume = {448}, number = {7157}, pages = {1050-3}, address = {England}, organization = {Perlegen Sciences, 2021 Stierlin Court, Mountain View, California 94043, USA. frazer.kelly@scrippshealth.org}, abstract = {A dense map of genetic variation in the laboratory mouse genome will provide insights into the evolutionary history of the species and lead to an improved understanding of the relationship between inter-strain genotypic and phenotypic differences. Here we resequence the genomes of four wild-derived and eleven classical strains. We identify 8.27 million high-quality single nucleotide polymorphisms (SNPs) densely distributed across the genome, and determine the locations of the high (divergent subspecies ancestry) and low (common subspecies ancestry) SNP-rate intervals for every pairwise combination of classical strains. Using these data, we generate a genome-wide haplotype map containing 40,898 segments, each with an average of three distinct ancestral haplotypes. For the haplotypes in the classical strains that are unequivocally assigned ancestry, the genetic contributions of the Mus musculus subspecies--M. m. domesticus, M. m. musculus, M. m. castaneus and the hybrid M. m. molossinus--are 68%, 6%, 3% and 10%, respectively; the remaining 13% of haplotypes are of unknown ancestral origin. The considerable regional redundancy of the SNP data will facilitate imputation of the majority of these genotypes in less-densely typed classical inbred strains to provide a complete view of variation in additional strains.}, keywords = {Mouse Genetics}, pubstate = {published}, tppubtype = {article} } A dense map of genetic variation in the laboratory mouse genome will provide insights into the evolutionary history of the species and lead to an improved understanding of the relationship between inter-strain genotypic and phenotypic differences. Here we resequence the genomes of four wild-derived and eleven classical strains. We identify 8.27 million high-quality single nucleotide polymorphisms (SNPs) densely distributed across the genome, and determine the locations of the high (divergent subspecies ancestry) and low (common subspecies ancestry) SNP-rate intervals for every pairwise combination of classical strains. Using these data, we generate a genome-wide haplotype map containing 40,898 segments, each with an average of three distinct ancestral haplotypes. For the haplotypes in the classical strains that are unequivocally assigned ancestry, the genetic contributions of the Mus musculus subspecies--M. m. domesticus, M. m. musculus, M. m. castaneus and the hybrid M. m. molossinus--are 68%, 6%, 3% and 10%, respectively; the remaining 13% of haplotypes are of unknown ancestral origin. The considerable regional redundancy of the SNP data will facilitate imputation of the majority of these genotypes in less-densely typed classical inbred strains to provide a complete view of variation in additional strains. |